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A Semantic Frame-Based Similarity Metric for Characterizing Technological Capabilities

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Language, Data, and Knowledge (LDK 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10318))

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Abstract

In this work we are motivated by the problem of representing technological capabilities that are present in text. We propose to use frames to capture the semantics around technologies and describe a new method, called FrameSim, that serves as a means of determining the similarity between these capabilities. We intentionally focus on a corpus built from informal media (e.g., news articles), which provides greater variability and an increased amount of suppositions about technologies’ uses, deriving value from ‘passive crowdsourcing’. Our evaluation shows that this semantic frame-based similarity metric preserves technology topic coherence, and we discuss how this method shows promise for improving conceptual search in scientific and technical writing.

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Notes

  1. 1.

    FrameGrapher is available at: https://framenet.icsi.berkeley.edu/fndrupal/FrameGrapher.

  2. 2.

    While the description of SimRank refers to documents and objects, here we utilize statements and frames.

  3. 3.

    This element is used in a study not discussed as part of this work, but included for completeness.

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Acknowledgement

This work was graciously funded by DTRA grant 1-15-1-0019.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Scott Appling & Erica Briscoe

Authors
  1. Scott Appling
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  2. Erica Briscoe
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Corresponding author

Correspondence to Scott Appling .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Jorge Gracia

  2. Nanyang Technological University, Singapore, Singapore

    Francis Bond

  3. Insight Centre for Data Analytics, National University of Ireland, Galway, Galway, Ireland

    John P. McCrae

  4. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Paul Buitelaar

  5. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos

  6. University of Leipzig, Leipzig, Germany

    Sebastian Hellmann

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Appling, S., Briscoe, E. (2017). A Semantic Frame-Based Similarity Metric for Characterizing Technological Capabilities. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-59888-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59887-1

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